# Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the FastVLM model.""" import copy import unittest import requests from transformers import ( AutoProcessor, FastVlmConfig, FastVlmForConditionalGeneration, FastVlmModel, is_torch_available, is_vision_available, ) from transformers.testing_utils import ( cleanup, require_torch, require_vision, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch if is_vision_available(): from PIL import Image class FastVlmVisionText2TextModelTester: def __init__( self, parent, ignore_index=-100, image_token_id=0, projector_hidden_act="gelu", seq_length=7, vision_feature_select_strategy="full", vision_feature_layer=-1, text_config={ "model_type": "qwen2", "is_training": True, "vocab_size": 99, "hidden_size": 32, "num_hidden_layers": 2, "num_attention_heads": 4, "num_key_value_heads": 4, "intermediate_size": 37, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 512, "initializer_range": 0.02, "pad_token_id": 1, }, is_training=True, vision_config={ "image_size": 16, "patch_size": 8, "num_channels": 3, "hidden_size": 32, "initializer_range": 0.02, "architecture": "fastvit_mci3", "do_pooling": True, "global_pool": "avg", "model_args": { "inference_mode": True, "layers": (2, 2), "embed_dims": (8, 16), "mlp_ratios": (4, 4), "se_downsamples": (False, False), "downsamples": (False, True), "pos_embs": (None, None), "token_mixers": ("repmixer", "repmixer"), "lkc_use_act": True, "stem_use_scale_branch": False, }, }, ): self.parent = parent self.ignore_index = ignore_index self.image_token_id = image_token_id self.projector_hidden_act = projector_hidden_act self.vision_feature_select_strategy = vision_feature_select_strategy self.vision_feature_layer = vision_feature_layer self.text_config = text_config self.vision_config = vision_config self.pad_token_id = text_config["pad_token_id"] self.num_hidden_layers = text_config["num_hidden_layers"] self.vocab_size = text_config["vocab_size"] self.hidden_size = text_config["hidden_size"] self.num_attention_heads = text_config["num_attention_heads"] self.is_training = is_training self.batch_size = 3 self.num_image_tokens = (self.vision_config["image_size"] // self.vision_config["patch_size"]) ** 2 self.seq_length = seq_length + self.num_image_tokens def get_config(self): return FastVlmConfig( text_config=self.text_config, vision_config=self.vision_config, ignore_index=self.ignore_index, image_token_id=self.image_token_id, projector_hidden_act=self.projector_hidden_act, vision_feature_select_strategy=self.vision_feature_select_strategy, vision_feature_layer=self.vision_feature_layer, ) def prepare_config_and_inputs(self): pixel_values = floats_tensor( [ self.batch_size, self.vision_config["num_channels"], self.vision_config["image_size"], self.vision_config["image_size"], ] ) config = self.get_config() return config, pixel_values def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1 input_ids[input_ids == config.image_token_index] = self.pad_token_id input_ids[:, : self.num_image_tokens] = config.image_token_index attention_mask = input_ids.ne(1).to(torch_device) inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class FastVlmForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): """ Model tester for `FastVlmForConditionalGeneration`. """ all_model_classes = ( ( FastVlmModel, FastVlmForConditionalGeneration, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"image-to-text": FastVlmForConditionalGeneration, "image-text-to-text": FastVlmForConditionalGeneration} if is_torch_available() else {} ) _is_composite = True def setUp(self): self.model_tester = FastVlmVisionText2TextModelTester(self) common_properties = ["image_token_id"] self.config_tester = ConfigTester( self, config_class=FastVlmConfig, has_text_modality=False, common_properties=common_properties ) def test_config(self): self.config_tester.run_common_tests() def test_mismatching_num_image_tokens(self): """ Tests that an explicit error is thrown when the number of image tokens doesn't match the number of image placeholders in the text. We also test multi-image cases when one prompt has multiple image tokens. """ config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config).to(torch_device) model.eval() curr_input_dict = copy.deepcopy(input_dict) # in-place modifications further _ = model(**curr_input_dict) # successful forward with no modifications # remove one image but leave all the image tokens in text curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][-2:, ...] with self.assertRaises(ValueError): _ = model(**curr_input_dict) # simulate the multi-image/single set of placeholders case by concatenating input_ids = curr_input_dict["input_ids"][:1] pixel_values = curr_input_dict["pixel_values"][:1] pixel_values = torch.cat([pixel_values, pixel_values], dim=0) # two images and one set of image tokens raise an error with self.assertRaises(ValueError): _ = model(input_ids=input_ids, pixel_values=pixel_values) # two images and two sets of image tokens don't raise an error input_ids = torch.cat([input_ids, input_ids], dim=0) _ = model(input_ids=input_ids, pixel_values=pixel_values) @unittest.skip("Timm wrapper and backbone don't currently support full HF initialization") def test_can_init_all_missing_weights(self): pass @unittest.skip("Timm can't be initialized on meta") def test_can_be_initialized_on_meta(self): pass @require_torch @slow class FastVlmForConditionalGenerationIntegrationTest(unittest.TestCase): def setUp(self): self.processor = AutoProcessor.from_pretrained("KamilaMila/FastVLM-0.5B") def tearDown(self): cleanup(torch_device, gc_collect=True) @require_vision def test_small_model_integration_test(self): model = FastVlmForConditionalGeneration.from_pretrained( "KamilaMila/FastVLM-0.5B", device_map=torch_device, dtype=torch.bfloat16 ) prompt = "user\n\nWhat are the things I should be cautious about when I visit this place?\nassistant" image_file = "https://llava-vl.github.io/static/images/view.jpg" raw_image = Image.open(requests.get(image_file, stream=True).raw) inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt").to(torch_device) output = model.generate(**inputs, max_new_tokens=20) expected_decoded_texts = "user\n\nWhat are the things I should be cautious about when I visit this place?\nassistant\n\nWhen visiting this place, there are a few things you should be cautious about:\n\n1. **" # fmt: skip EXPECTED_DECODED_TEXT = expected_decoded_texts self.assertEqual( self.processor.decode(output[0], skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @require_vision def test_small_model_integration_test_batch(self): model = FastVlmForConditionalGeneration.from_pretrained( "KamilaMila/FastVLM-0.5B", device_map=torch_device, dtype=torch.bfloat16 ) prompts = [ "user\n\nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nassistant", "user\n\nWhat is this?\nassistant", ] image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw) image2 = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) inputs = self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True).to( torch_device ) output = model.generate(**inputs, max_new_tokens=20) EXPECTED_DECODED_TEXT = [ "user\n\nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nassistant\n\nWhen visiting this serene place, it's essential to be mindful of the following:\n\n1. **", "user\n\nWhat is this?\nassistant\nThe image depicts two cats lying on a pink surface, which could be a couch or a" ] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) def test_generation_no_images(self): model_id = "KamilaMila/FastVLM-0.5B" model = FastVlmForConditionalGeneration.from_pretrained( model_id, device_map=torch_device, dtype=torch.bfloat16 ) processor = AutoProcessor.from_pretrained(model_id) # Prepare inputs with no images inputs = processor(text="Hello, I am", return_tensors="pt").to(torch_device) # Make sure that `generate` works _ = model.generate(**inputs, max_new_tokens=20)